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Table 6 Classification results of the proposed technique compared to simple feature fusion with four datasets using F-KNN, ES-KNN and SVM

From: A multilevel features selection framework for skin lesion classification

Vector Fusion

OA (%)

Feature Fusion Approach

Proposed (ECNCA)

F-KNN

SVM

ES-KNN

F-KNN

SVM

ES-KNN

\(\hbox {PH}^{2}\)

FV0-FV1

82.8

80.0

80.1

96.9

93.7

95.1

FV0-FV2

82.1

81.7

81.7

95.1

94.8

93.2

FV1-FV2

82.9

82.0

82.1

97.4

95.0

97.1

FV0-FV1-FV2

83.2

82.2

82.4

\(98.8*\)

95.1

98.1

ISIC-MSK

FV0-FV1

74.2

71.7

74.6

93.7

87.2

88.8

FV0-FV2

73.9

71.0

73.0

89.1

89.0

87.4

FV1-FV2

73.1

72.5

75.1

86.5

91.0

89.7

FV0-FV1-FV2

76.4

74.8

74.9

\(99.2*\)

95.1

96.9

ISIC-UDA

FV0-FV1

71.9

70.0

75.9

88.8

80.1

84.7

FV0-FV2

73.3

71.2

74.1

90.7

84.5

88.0

FV1-FV2

74.1

75.9

75.8

92.8

82.7

94.2

FV0-FV1-FV2

76.5

73.5

76.0

\(97.1*\)

93.3

95.7

ISBI-2017

FV0-FV1

73.2

70.9

71.1

88.5

88.0

88.8

FV0-FV2

74.7

70.7

72.8

89.7

87.3

89.3

FV1-FV2

72.1

70.5

73.3

90.0

88.9

90.7

FV0-FV1-FV2

75.3

75.1

76.1

94.1

93.4

\(95.9*\)

  1. * Shows the highest value in each dataset